Gordon B. Bonan

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.

Abstract The Community Climate System Model version 3 (CCSM3) has recently been developed and released to the climate community. CCSM3 is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to produce realistic simulations over a wide range of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed studies of continental-scale dynamics, variability, and climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for the atmosphere and land and a grid with approximately 1° resolution for the ocean and sea ice. The new system incorporates several significant improvements in the physical parameterizations. The enhancements in the model physics are designed to reduce or eliminate several systematic biases in the mean climate produced by previous editions of CCSM. These include new treatments of cloud processes, aerosol radiative forcing, land–atmosphere fluxes, ocean mixed layer processes, and sea ice dynamics. There are significant improvements in the sea ice thickness, polar radiation budgets, tropical sea surface temperatures, and cloud radiative effects. CCSM3 can produce stable climate simulations of millennial duration without ad hoc adjustments to the fluxes exchanged among the component models. Nonetheless, there are still systematic biases in the ocean–atmosphere fluxes in coastal regions west of continents, the spectrum of ENSO variability, the spatial distribution of precipitation in the tropical oceans, and continental precipitation and surface air temperatures. Work is under way to extend CCSM to a more accurate and comprehensive model of the earth's climate system.

Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land−a key diagnostic criterion of the effects of climate change and variability−remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].

The latest version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) is described. The changes in both physical and dynamical formulation from CCM2 to CCM3 are presented. The major differences in CCM3 compared to CCM2 include changes to the parameterization of cloud properties, clear sky longwave radiation, deep convection, boundary layer processes, and land surface processes. A brief description of each of these parameterization changes is provided. These modifications to model physics have led to dramatic improvements in the simulated climate of the CCM. In particular, the top of atmosphere cloud radiative forcing is now in good agreement with observations, the Northern Hemisphere winter dynamical simulation has significantly improved, biases in surface land temperatures and precipitation have been substantially reduced, and the implied ocean heat transport is in very good agreement with recent observational estimates. The improvement in implied ocean heat transport is among the more important attributes of the CCM3 since it is used as the atmospheric component of the NCAR Climate System Model. Future improvements to the CCM3 are also discussed.

Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.

Earth system models (ESMs) generally have crude representations of the responses of soil carbon responses to changing climate. Now an ESM that explicitly represents microbial soil carbon cycling mechanisms is able to simulate carbon pools that closely match observations. Projections from this model produce a much wider range of soil carbon responses to climate change over the twenty-first century than conventional ESMs. Society relies on Earth system models (ESMs) to project future climate and carbon (C) cycle feedbacks. However, the soil C response to climate change is highly uncertain in these models1,2 and they omit key biogeochemical mechanisms3,4,5. Specifically, the traditional approach in ESMs lacks direct microbial control over soil C dynamics6,7,8. Thus, we tested a new model that explicitly represents microbial mechanisms of soil C cycling on the global scale. Compared with traditional models, the microbial model simulates soil C pools that more closely match contemporary observations. It also projects a much wider range of soil C responses to climate change over the twenty-first century. Global soils accumulate C if microbial growth efficiency declines with warming in the microbial model. If growth efficiency adapts to warming, the microbial model projects large soil C losses. By comparison, traditional models project modest soil C losses with global warming. Microbes also change the soil response to increased C inputs, as might occur with CO2 or nutrient fertilization. In the microbial model, microbes consume these additional inputs; whereas in traditional models, additional inputs lead to C storage. Our results indicate that ESMs should simulate microbial physiology to more accurately project climate change feedbacks.

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, ...Read More

The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). CLM3 has energy and water biases resulting from deficiencies in some of its canopy and soil parameterizations related to hydrological processes. Recent research by the community that utilizes CLM3 and the family of CCSM models has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL‐based model for surface and subsurface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity. The results from a set of offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5). CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage. Phase and amplitude of the runoff annual cycle is generally improved. Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency.

Abstract The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.

Abstract. The land and ocean absorb on average just over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 "sinks" are modulated by climate change and variability. Here we use a suite of nine dynamic global vegetation models (DGVMs) and four ocean biogeochemical general circulation models (OBGCMs) to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990–2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement. The models were forced with reconstructed climate fields and observed global atmospheric CO2; land use and land cover changes are not included for the DGVMs. Over the period 1990–2009, the DGVMs simulate a mean global land carbon sink of −2.4 ± 0.7 Pg C yr−1 with a small significant trend of −0.06 ± 0.03 Pg C yr−2 (increasing sink). Over the more limited period 1990–2004, the ocean models simulate a mean ocean sink of −2.2 ± 0.2 Pg C yr−1 with a trend in the net C uptake that is indistinguishable from zero (−0.01 ± 0.02 Pg C yr−2). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small, trend of −0.02 ± 0.01 Pg C yr−2. Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink are driven by increasing net primary production (NPP), whose statistically significant trend of 0.22 ± 0.08 Pg C yr−2 exceeds a significant trend in heterotrophic respiration of 0.16 ± 0.05 Pg C yr−2 – primarily as a consequence of widespread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (−0.04 ± 0.01 Pg C yr−2), with almost no trend over the northern land region, where recent warming and reduced rainfall offsets the positive impact of elevated atmospheric CO2 and changes in growing season length on carbon storage. The small uptake trend in the ocean models emerges because climate variability and change, and in particular increasing sea surface temperatures, tend to counter\\-act the trend in ocean uptake driven by the increase in atmospheric CO2. Large uncertainty remains in the magnitude and sign of modelled carbon trends in several regions, as well as regarding the influence of land use and land cover changes on regional trends.

Abstract The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon–climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon–concentration and carbon–climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon–concentration feedback to diagnosed cumulative emissions that are consistent with the 1% increasing CO2 concentration scenario is about 4.5 times larger than the carbon–climate feedback. Differences in the modeled responses of the carbon budget to changes in CO2 and temperature are seen to be 3–4 times larger for the land components compared to the ocean components of participating models. The feedback parameters depend on the state of the system as well the forcing scenario but nevertheless provide insight into the behavior of the coupled carbon–climate system and a useful common framework for comparing models.

The Community Land Model is the land component of the Community Climate System Model.Here, we describe a broad set of model improvements and additions that have been provided through the CLM development community to create CLM4.The model is extended with a carbon-nitrogen (CN) biogeochemical model that is prognostic with respect to vegetation, litter, and soil carbon and nitrogen states and vegetation phenology.An urban canyon model is added and a transient land cover and land use change (LCLUC) capability, including wood harvest, is introduced, enabling study of historic and future LCLUC on energy, water, momentum, carbon, and nitrogen fluxes.The hydrology scheme is modified with a revised numerical solution of the Richards equation and a revised ground evaporation parameterization that accounts for litter and within-canopy stability.The new snow model incorporates the SNow and Ice Aerosol Radiation model (SNICAR) -which includes aerosol deposition, grain-size dependent snow aging, and vertically-resolved snowpack heating -as well as new snow cover and snow burial fraction parameterizations.The thermal and hydrologic properties of organic soil are accounted for and the ground column is extended to ,50-m depth.Several other minor modifications to the land surface types dataset, grass and crop optical properties, surface layer thickness, roughness length and displacement height, and the disposition of snow-capped runoff are also incorporated.The new model exhibits higher snow cover, cooler soil temperatures in organic-rich soils, greater global river discharge, and lower albedos over forests and grasslands, all of which are improvements compared to CLM3.5.When CLM4 is run with CN, the mean biogeophysical simulation is degraded because the vegetation structure is prognostic rather than prescribed, though running in this mode also allows more complex terrestrial interactions with climate and climate change.

The land surface parameterization used with the community climate model (CCM3) and the climate system model (CSM1), the National Center for Atmospheric Research land surface model (NCAR LSM1), has been modified as part of the development of the next version of these climate models. This new model is known as the community land model (CLM2). In CLM2, the surface is represented by five primary subgrid land cover types (glacier, lake, wetland, urban, vegetated) in each grid cell. The vegetated portion of a grid cell is further divided into patches of up to 4 of 16 plant functional types, each with its own leaf and stem area index and canopy height. The relative area of each subgrid unit, the plant functional type, and leaf area index are obtained from 1-km satellite data. The soil texture dataset allows vertical profiles of sand and clay. Most of the physical parameterizations in the model were also updated. Major model differences include: 10 layers for soil temperature and soil water with explicit treatment of liquid water and ice; a multilayer snowpack; runoff based on the TOPMODEL concept; new formulation of ground and vegetation fluxes; and vertical root profiles from a global synthesis of ecological studies. Simulations with CCM3 show significant improvements in surface air temperature, snow cover, and runoff for CLM2 compared to LSM1. CLM2 generally warms surface air temperature in all seasons compared to LSM1, reducing or eliminating many cold biases. Annual precipitation over land is reduced from 2.35 mm day−1 in LSM1 to 2.14 mm day−1 in CLM2. The hydrologic cycle is also different. Transpiration and ground evaporation are reduced. Leaves and stems evaporate more intercepted water annually in CLM2 than LSM1. Global runoff from land increases from 0.75 mm day−1 in LSM1 to 0.84 mm day−1 in CLM2. The annual cycle of runoff is greatly improved in CLM2, especially in arctic and boreal regions where the model has low runoff in cold seasons when the soil is frozen and high runoff during the snowmelt season. Most of the differences between CLM2 and LSM1 are attributed to particular parameterizations rather than to different surface datasets. Important processes include: multilayer snow, frozen water, interception, soil water limitation to latent heat, and higher aerodynamic resistances to heat exchange from ground.

While most land models developed for use with climate models represent vegetation as discrete biomes, this is, at least for mixed life‐form biomes, inconsistent with the leaf‐level and whole‐plant physiological parameterizations needed to couple these biogeophysical models with biogeochemical and ecosystem dynamics models. In this paper, we present simulations with the National Center for Atmospheric Research land surface model (NCAR LSM) that examined the effect of representing vegetation as patches of plant functional types (PFTs) that coexist within a model grid cell. This approach is consistent with ecological theory and models and allows for unified treatment of vegetation in climate and ecosystem models. In the standard NCAR LSM the PFT composition and leaf area for each grid cell are obtained by classifying grid cells as 1 of 28 possible biomes. Here, we develop a data set from 1‐km satellite data that provides each model grid cell a unique PFT composition and leaf area for each PFT. Global simulations at 3° × 3° spatial resolution showed that ground temperature, ground evaporation, and northern high‐latitude winter albedo exhibited direct responses to these landscape changes, which led to indirect effects such as in soil moisture and sensible and latent heat fluxes. Additional simulations at 2° × 2° and 1° × 1° spatial resolution showed that low‐resolution simulations masked landscape heterogeneity in both approaches but the satellite‐based, continuous representation of vegetation reduced model sensitivity to resolution. It is argued that the use of spatially continuous distributions of coexisting PFTs is a necessary step to link climate and ecosystem models.

Planetary warming may be exacerbated if it accelerates loss of soil carbon to the atmosphere. This carbon-cycle–climate feedback is included in climate projections. Yet, despite ancillary data supporting a positive feedback, there is limited evidence for soil carbon loss under warming. The low confidence engendered in feedback projections is reduced further by the common representation in models of an outdated knowledge of soil carbon turnover. 'Model-knowledge integration' — representing in models an advanced understanding of soil carbon stabilization — is the first step to build confidence. This will inform experiments that further increase confidence by resolving competing mechanisms that most influence projected soil-carbon stocks. Improving feedback projections is an imperative for establishing greenhouse gas emission targets that limit climate change. Climate change may accelerate decomposition of soil carbon leading to a reinforcing cycle of further warming and soil carbon loss. This Review considers the uncertainties and modelling challenges involved in projecting soil responses to warming.

Seven climate models were used to explore the biogeophysical impacts of human‐induced land cover change (LCC) at regional and global scales. The imposed LCC led to statistically significant decreases in the northern hemisphere summer latent heat flux in three models, and increases in three models. Five models simulated statistically significant cooling in summer in near‐surface temperature over regions of LCC and one simulated warming. There were few significant changes in precipitation. Our results show no common remote impacts of LCC. The lack of consistency among the seven models was due to: 1) the implementation of LCC despite agreed maps of agricultural land, 2) the representation of crop phenology, 3) the parameterisation of albedo, and 4) the representation of evapotranspiration for different land cover types. This study highlights a dilemma: LCC is regionally significant, but it is not feasible to impose a common LCC across multiple models for the next IPCC assessment.

Abstract Although the global partitioning of evapotranspiration (ET) into transpiration, soil evaporation, and canopy evaporation is not well known, most current land surface schemes and the few available observations indicate that transpiration is the dominant component on the global scale, followed by soil evaporation and canopy evaporation. The Community Land Model version 3 (CLM3), however, does not reflect this global view of ET partitioning, with soil evaporation and canopy evaporation far outweighing transpiration. One consequence of this unrealistic ET partitioning in CLM3 is that photosynthesis, which is linked to transpiration through stomatal conductance, is significantly underestimated on a global basis. A number of modifications to CLM3 vegetation and soil hydrology parameterizations are described that improve ET partitioning and reduce an apparent dry soil bias in CLM3. The modifications reduce canopy interception and evaporation, reduce soil moisture stress on transpiration, increase transpiration through a more realistic canopy integration scheme, reduce within-canopy soil evaporation, eliminate lateral drainage of soil water, increase infiltration of water into the soil, and increase the vertical redistribution of soil water. The partitioning of ET is improved, with notable increases seen in transpiration (13%–41% of global ET) and photosynthesis (65–148 Pg C yr−1). Soils are wetter and exhibit a far more distinct soil moisture annual cycle and greater interseasonal soil water storage, which permits plants to sustain transpiration through the dry season. The broader influences of improved ET partitioning on land–atmosphere interaction are diverse. Stronger transpiration and reduced canopy evaporation yield an extended ET response to rain events and a shift in the precipitation distribution toward more frequent small- to medium-size rain events. Soil moisture memory time scales decrease particularly at deeper soil levels. Subsurface soil moisture exerts a slightly greater influence on precipitation. These results indicate that partitioning of ET is an important responsibility for land surface schemes, a responsibility that will gain in relevance as GCMs evolve to incorporate ever more complex treatments of the earth’s carbon and hydrologic cycles.

The third edition of Gordon Bonan's comprehensive textbook introduces an interdisciplinary framework to understand the interaction between terrestrial ecosystems and climate change. Ideal for advanced undergraduate and graduate students studying ecology, environmental science, atmospheric science, and geography, it reviews basic meteorological, hydrological, and ecological concepts to examine the physical, chemical, and biological processes by which terrestrial ecosystems affect and are affected by climate. This new edition has been thoroughly updated with new science and references. The scope has been expanded beyond its initial focus on energy, water, and carbon to include reactive gases and aerosols in the atmosphere. The new edition emphasizes the Earth as a system, recognizing interconnections among the planet's physical, chemical, biological, and socioeconomic components, and emphasizing global environmental sustainability. Each chapter contains chapter summaries and review questions, and with over 400 illustrations, including many in color, this textbook will once again be an essential student guide.

Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO2 assimilation (A) to key environmental variables: light, temperature, CO2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.

Abstract Changes in vegetation structure and biogeography due to climate change feedback to alter climate by changing fluxes of energy, moisture, and momentum between land and atmosphere. While the current class of land process models used with climate models parameterizes these fluxes in detail, these models prescribe surface vegetation and leaf area from data sets. In this paper, we describe an approach in which ecological concepts from a global vegetation dynamics model are added to the land component of a climate model to grow plants interactively. The vegetation dynamics model is the Lund–Potsdam–Jena (LPJ) dynamic global vegetation model. The land model is the National Center for Atmospheric Research (NCAR) Land Surface Model (LSM). Vegetation is defined in terms of plant functional types. Each plant functional type is represented by an individual plant with the average biomass, crown area, height, and stem diameter (trees only) of its population, by the number of individuals in the population, and by the fractional cover in the grid cell. Three time‐scales (minutes, days, and years) govern the processes. Energy fluxes, the hydrologic cycle, and carbon assimilation, core processes in LSM, occur at a 20 min time step. Instantaneous net assimilated carbon is accumulated annually to update vegetation once a year. This is carried out with the addition of establishment, resource competition, growth, mortality, and fire parameterizations from LPJ. The leaf area index is updated daily based on prevailing environmental conditions, but the maximum value depends on the annual vegetation dynamics. The coupling approach is successful. The model simulates global biogeography, net primary production, and dynamics of tundra, boreal forest, northern hardwood forest, tropical rainforest, and savanna ecosystems, which are consistent with observations. This suggests that the model can be used with a climate model to study biogeophysical feedbacks in the climate system related to vegetation dynamics.

The boreal forests of the world, geographically situated to the south of the Arctic and generally north of latitude 50 degrees, are considered to be one of the earth's most significant terrestrial ecosystems in terms of their potential for interaction with other global scale systems, such as climate and anthropologenic activity. This book, developed by an international panel of ecologists, provides a synthesis of the important patterns and processes which occur in boreal forests and reviews the principal mechanisms which control the forests' pattern in space and time. The effects of cold temperatures, soil ice, insects, plant competition, wildfires and climatic change on the boreal forests are discussed as a basis for the development of the first global scale computer model of the dynamical change of a biome, able to project the change of the boreal forest over timescales of decades to millennia, and over the global extent of this forest.

Abstract The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.

Abstract With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon‐LAnd Model Intercomparison Project (C‐LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) – Carnegie‐Ames‐Stanford Approach′ (CASA′) and carbon–nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO 2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1–3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO 2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4 Pg C yr −1 for CASA′ vs. 1.2 Pg C yr −1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988–2004 based on comparison with atmospheric inversion results from TRANSCOM ( r =0.66 for CASA′ and r =0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C‐LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community‐wide platform for model‐data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C‐LAMP are publicly available on the Earth System Grid.

Policies for climate mitigation on land rarely acknowledge biophysical factors, such as reflectivity, evaporation, and surface roughness. Yet such factors can alter temperatures much more than carbon sequestration does, and often in a conflicting way. We outline a framework for examining biophysical factors in mitigation policies and provide some best-practice recommendations based on that framework. Tropical projects—avoided deforestation, forest restoration, and afforestation—provide the greatest climate value, because carbon storage and biophysics align to cool the Earth. In contrast, the climate benefits of carbon storage are often counteracted in boreal and other snow-covered regions, where darker trees trap more heat than snow does. Managers can increase the climate benefit of some forest projects by using more reflective and deciduous species and through urban forestry projects that reduce energy use. Ignoring biophysical interactions could result in millions of dollars being invested in some mitigation projects that provide little climate benefit or, worse, are counter-productive.

Abstract Several multidecadal simulations have been carried out with the new version of the Community Climate System Model (CCSM). This paper reports an analysis of the land component of these simulations. Global annual averages over land appear to be within the uncertainty of observational datasets, but the seasonal cycle over land of temperature and precipitation appears to be too weak. These departures from observations appear to be primarily a consequence of deficiencies in the simulation of the atmospheric model rather than of the land processes. High latitudes of northern winter are biased sufficiently warm to have a significant impact on the simulated value of global land temperature. The precipitation is approximately doubled from what it should be at some locations, and the snowpack and spring runoff are also excessive. The winter precipitation over Tibet is larger than observed. About two-thirds of this precipitation is sublimated during the winter, but what remains still produces a snowpack that is very large compared to that observed with correspondingly excessive spring runoff. A large cold anomaly over the Sahara Desert and Sahel also appears to be a consequence of a large anomaly in downward longwave radiation; low column water vapor appears to be most responsible. The modeled precipitation over the Amazon basin is low compared to that observed, the soil becomes too dry, and the temperature is too warm during the dry season.

The project Land-Use and Climate, Identification of Robust Impacts (LUCID) was conceived to address the robustness of biogeophysical impacts of historical land use–land cover change (LULCC). LUCID used seven atmosphere–land models with a common experimental design to explore those impacts of LULCC that are robust and consistent across the climate models. The biogeophysical impacts of LULCC were also compared to the impact of elevated greenhouse gases and resulting changes in sea surface temperatures and sea ice extent (CO2SST). Focusing the analysis on Eurasia and North America, this study shows that for a number of variables LULCC has an impact of similar magnitude but of an opposite sign, to increased greenhouse gases and warmer oceans. However, the variability among the individual models’ response to LULCC is larger than that found from the increase in CO2SST. The results of the study show that although the dispersion among the models’ response to LULCC is large, there are a number of robust common features shared by all models: the amount of available energy used for turbulent fluxes is consistent between the models and the changes in response to LULCC depend almost linearly on the amount of trees removed. However, less encouraging is the conclusion that there is no consistency among the various models regarding how LULCC affects the partitioning of available energy between latent and sensible heat fluxes at a specific time. The results therefore highlight the urgent need to evaluate land surface models more thoroughly, particularly how they respond to a perturbation in addition to how they simulate an observed average state.

Abstract This paper reviews developments for the Community Land Model, version 4 (CLM4), examines the land surface climate simulation of the Community Climate System Model, version 4 (CCSM4) compared to CCSM3, and assesses new earth system features of CLM4 within CCSM4. CLM4 incorporates a broad set of improvements including additions of a carbon–nitrogen (CN) biogeochemical model, an urban canyon model, and transient land cover and land use change, as well as revised soil and snow submodels. Several aspects of the surface climate simulation are improved in CCSM4. Improvements in the simulation of soil water storage, evapotranspiration, surface albedo, and permafrost that are apparent in offline CLM4 simulations are generally retained in CCSM4. The global land air temperature bias is reduced and the annual cycle is improved in many locations, especially at high latitudes. The global land precipitation bias is larger in CCSM4 because of bigger wet biases in central and southern Africa and Australia. New earth system capabilities are assessed. The present-day air temperature within urban areas is warmer than surrounding rural areas by 1°–2°C, which is comparable to or greater than the change in climate occurring over the last 130 years. The snow albedo feedback is more realistic and the radiative forcing of snow aerosol deposition is calculated as +0.083 W m−2 for present day. The land carbon flux due to land use, wildfire, and net ecosystem production is a source of carbon to the atmosphere throughout most of the historical simulation. CCSM4 is increasingly suited for studies of the role of land processes in climate and climate change.

This book introduces an interdisciplinary framework to understand the interaction between terrestrial ecosystems and climate change. It reviews basic meteorological, hydrological and ecological concepts to examine the physical, chemical and biological processes by which terrestrial ecosystems affect and are affected by climate. The textbook is written for advanced undergraduate and graduate students studying ecology, environmental science, atmospheric science and geography. The central argument is that terrestrial ecosystems become important determinants of climate through their cycling of energy, water, chemical elements and trace gases. This coupling between climate and vegetation is explored at spatial scales from plant cells to global vegetation geography and at timescales of near instantaneous to millennia. The text also considers how human alterations to land become important for climate change. This restructured edition, with updated science and references, chapter summaries and review questions, and over 400 illustrations, including many in colour, serves as an essential student guide.

Forestry – including afforestation (the planting of trees on land where they have not recently existed), reforestation, avoided deforestation, and forest management – can lead to increased sequestration of atmospheric carbon dioxide and has therefore been proposed as a strategy to mitigate climate change. However, forestry also influences land‐surface properties, including albedo (the fraction of incident sunlight reflected back to space), surface roughness, and evapotranspiration, all of which affect the amount and forms of energy transfer to the atmosphere. In some circumstances, these biophysical feedbacks can result in local climate warming, thereby counteracting the effects of carbon sequestration on global mean temperature and reducing or eliminating the net value of climate‐change mitigation projects. Here, we review published and emerging research that suggests ways in which forestry projects can counteract the consequences associated with biophysical interactions, and highlight knowledge gaps in managing forests for climate protection. We also outline several ways in which biophysical effects can be incorporated into frameworks that use the maintenance of forests as a climate protection strategy.

Arctic climate is projected to change dramatically in the next 100 years and increases in temperature will likely lead to changes in the distribution and makeup of the Arctic biosphere. A largely deciduous ecosystem has been suggested as a possible landscape for future Arctic vegetation and is seen in paleo-records of warm times in the past. Here we use a global climate model with an interactive terrestrial biosphere to investigate the effects of adding deciduous trees on bare ground at high northern latitudes. We find that the top-of-atmosphere radiative imbalance from enhanced transpiration (associated with the expanded forest cover) is up to 1.5 times larger than the forcing due to albedo change from the forest. Furthermore, the greenhouse warming by additional water vapor melts sea-ice and triggers a positive feedback through changes in ocean albedo and evaporation. Land surface albedo change is considered to be the dominant mechanism by which trees directly modify climate at high-latitudes, but our findings suggest an additional mechanism through transpiration of water vapor and feedbacks from the ocean and sea-ice.

To assess the climate impacts of historical and projected land cover change in the Community Climate System Model, version 4 (CCSM4), new time series of transient Community Land Model, version 4 (CLM4) plant functional type (PFT) and wood harvest parameters have been developed. The new parameters capture the dynamics of the Coupled Model Intercomparison Project phase 5 (CMIP5) land cover change and wood harvest trajectories for the historical period from 1850 to 2005 and for the four representative concentration pathway (RCP) scenarios from 2006 to 2100. Analysis of the biogeochemical impacts of land cover change in CCSM4 reveals that the model produced a historical cumulative land use flux of 127.7 PgC from 1850 to 2005, which is in general agreement with other global estimates of 156 PgC for the same period. The biogeophysical impacts of the transient land cover change parameters were cooling of the near-surface atmosphere over land by −0.1°C, through increased surface albedo and reduced shortwave radiation absorption. When combined with other transient climate forcings, the higher albedo from land cover change was counteracted by decreasing snow albedo from black carbon deposition and high-latitude warming. The future CCSM4 RCP simulations showed that the CLM4 transient PFT parameters can be used to represent a wide range of land cover change scenarios. In the reforestation scenario of RCP 4.5, CCSM4 simulated a drawdown of 67.3 PgC from the atmosphere into the terrestrial ecosystem and product pools. By contrast the RCP 8.5 scenario with deforestation and high wood harvest resulted in the release of 30.3 PgC currently stored in the ecosystem.

Abstract. A growing body of literature documents the pressing need to develop soil biogeochemistry models that more accurately reflect contemporary understanding of soil processes and better capture soil carbon (C) responses to environmental perturbations. Models that explicitly represent microbial activity offer inroads to improve representations of soil biogeochemical processes, but have yet to consider relationships between litter quality, functional differences in microbial physiology, and the physical protection of microbial byproducts in forming stable soil organic matter (SOM). To address these limitations, we introduce the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, and evaluate it by comparing site-level soil C projections with observations from a long-term litter decomposition study and soil warming experiment. In MIMICS, the turnover of litter and SOM pools is governed by temperature-sensitive Michaelis–Menten kinetics and the activity of two physiologically distinct microbial functional types. The production of microbial residues through microbial turnover provides inputs to SOM pools that are considered physically or chemically protected. Soil clay content determines the physical protection of SOM in different soil environments. MIMICS adequately simulates the mean rate of leaf litter decomposition observed at temperate and boreal forest sites, and captures observed effects of litter quality on decomposition rates. Moreover, MIMICS better captures the response of SOM pools to experimental warming, with rapid SOM losses but declining temperature sensitivity to long-term warming, compared with a more conventional model structure. MIMICS incorporates current microbial theory to explore the mechanisms by which litter C is converted to stable SOM, and to improve predictions of soil C responses to environmental change.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

The third edition of Gordon Bonan's comprehensive textbook introduces an interdisciplinary framework to understand the interaction between terrestrial ecosystems and climate change. Ideal for advanced undergraduate and graduate students studying ecology, environmental science, atmospheric science, and geography, it reviews basic meteorological, hydrological, and ecological concepts to examine the physical, chemical, and biological processes by which terrestrial ecosystems affect and are affected by climate. This new edition has been thoroughly updated with new science and references. The scope has been expanded beyond its initial focus on energy, water, and carbon to include reactive gases and aerosols in the atmosphere. The new edition emphasizes the Earth as a system, recognizing interconnections among the planet's physical, chemical, biological, and socioeconomic components, and emphasizing global environmental sustainability. Each chapter contains chapter summaries and review questions, and with over 400 illustrations, including many in color, this textbook will once again be an essential student guide.

Abstract. We describe an implementation of the Ecosystem Demography (ED) concept in the Community Land Model. The structure of CLM(ED) and the physiological and structural modifications applied to the CLM are presented. A major motivation of this development is to allow the prediction of biome boundaries directly from plant physiological traits via their competitive interactions. Here we investigate the performance of the model for an example biome boundary in eastern North America. We explore the sensitivity of the predicted biome boundaries and ecosystem properties to the variation of leaf properties using the parameter space defined by the GLOPNET global leaf trait database. Furthermore, we investigate the impact of four sequential alterations to the structural assumptions in the model governing the relative carbon economy of deciduous and evergreen plants. The default assumption is that the costs and benefits of deciduous vs. evergreen leaf strategies, in terms of carbon assimilation and expenditure, can reproduce the geographical structure of biome boundaries and ecosystem functioning. We find some support for this assumption, but only under particular combinations of model traits and structural assumptions. Many questions remain regarding the preferred methods for deployment of plant trait information in land surface models. In some cases, plant traits might best be closely linked to each other, but we also find support for direct linkages to environmental conditions. We advocate intensified study of the costs and benefits of plant life history strategies in different environments and the increased use of parametric and structural ensembles in the development and analysis of complex vegetation models.

Earth System Models typically use static responses to temperature to calculate photosynthesis and respiration, but experimental evidence suggests that many plants acclimate to prevailing temperatures. We incorporated representations of photosynthetic and leaf respiratory temperature acclimation into the Community Land Model, the terrestrial component of the Community Earth System Model. These processes increased terrestrial carbon pools by 20 Pg C (22%) at the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) climate scenario. Including the less certain estimates of stem and root respiration acclimation increased terrestrial carbon pools by an additional 17 Pg C (~40% overall increase). High latitudes gained the most carbon with acclimation, and tropical carbon pools increased least. However, results from both of these regions remain uncertain; few relevant data exist for tropical and boreal plants or for extreme temperatures. Constraining these uncertainties will produce more realistic estimates of land carbon feedbacks throughout the 21st century.

Abstract. Projecting biogeochemical responses to global environmental change requires multi-scaled perspectives that consider organismal diversity, ecosystem processes, and global fluxes. However, microbes, the drivers of soil organic matter decomposition and stabilization, remain notably absent from models used to project carbon (C) cycle–climate feedbacks. We used a microbial trait-based soil C model with two physiologically distinct microbial communities, and evaluate how this model represents soil C storage and response to perturbations. Drawing from the application of functional traits used to model other ecosystems, we incorporate copiotrophic and oligotrophic microbial functional groups in the MIcrobial-MIneral Carbon Stabilization (MIMICS) model; these functional groups are akin to "gleaner" vs. "opportunist" plankton in the ocean, or r- vs. K-strategists in plant and animal communities. Here we compare MIMICS to a conventional soil C model, DAYCENT (the daily time-step version of the CENTURY model), in cross-site comparisons of nitrogen (N) enrichment effects on soil C dynamics. MIMICS more accurately simulates C responses to N enrichment; moreover, it raises important hypotheses involving the roles of substrate availability, community-level enzyme induction, and microbial physiological responses in explaining various soil biogeochemical responses to N enrichment. In global-scale analyses, we show that MIMICS projects much slower rates of soil C accumulation than a conventional soil biogeochemistry in response to increasing C inputs with elevated carbon dioxide (CO2) – a finding that would reduce the size of the land C sink estimated by the Earth system. Our findings illustrate that tradeoffs between theory and utility can be overcome to develop soil biogeochemistry models that evaluate and advance our theoretical understanding of microbial dynamics and soil biogeochemical responses to environmental change.

The National Center for Atmospheric Research (NCAR) Land Surface Model (LSM, version 1.0) provides a comprehensive treatment of land surface processes for the NCAR Community Climate Model version 3 (CCM3). It replaces the prescribed surface wetness, prescribed snow cover, surface albedo, and surface flux parameterizations used in the CCM2. A 15-yr simulation of the coupled atmosphere (CCM3) and land (LSM1.0) models using observed sea surface temperatures for the period December 1978–September 1993 is used to document the model’s land surface climate. The model simulates many of the observed geographic and seasonal patterns of surface air temperature, precipitation, and soil water. In general, the transition seasons (spring, autumn) are better simulated than winter and summer. Annual precipitation and runoff are well simulated for some river basins and poorly simulated for others. In general, precipitation is better simulated than runoff. The inclusion of net land–atmosphere CO2 exchange is an important component of the land model, allowing it to be used for studies of the global carbon cycle. The model simulates annual net primary production that is consistent with other estimates of annual production. The model also simulates a clearly defined growing season based on temperature and soil water.

CO 2 uptake during plant photosynthesis and CO 2 loss during plant and microbial respiration were added to a land surface process model to simulate the diurnal and annual cycles of biosphere‐atmosphere CO 2 exchange. The model was coupled to a modified version of the National Center for Atmospheric Research Community Climate Model version 2, and the coupled model was run for 5 years. The geographic patterns of annual net primary production are qualitatively similar to other models. When compared by vegetation type, annual production and annual microbial respiration are consistent with other models, except for needleleaf evergreen tree vegetation, where production is too high, and semidesert vegetation, where production and microbial respiration are too low. The seasonally of the net CO 2 flux agrees with other models in the southern hemisphere and the tropics. The diurnal range is large for photosynthesis and lower for plant and microbial respiration, which agrees with qualitative expectations. The simulation of the central United States is poor due to temperature and precipitation biases in the coupled model. Despite these deficiencies the current approach is a promising means to include terrestrial CO 2 fluxes in a climate system model that simulates atmospheric CO 2 concentrations, because it alleviates important parameterization discrepancies between standard biogeochemical models and the land surface models typically used in general circulation models, and because the model resolves the diurnal range of CO 2 exchange, which can be large (15–45 μmol CO 2 m −2 s −1 ).

Leaf area index (LAI) and vegetation type are two ecological variables that influence atmosphere-biosphere exchange of CO2 and that can be estimated from remote sensing techniques. A forest ecosystem process model was used to examine the importance of LAI and species-dependent physiology when estimating photosynthesis in 21 black spruce, white spruce, quaking aspen, paper birch, and balsam poplar forests near Fairbanks, Alaska. Model sensitivity analyses for these 21 stands showed that uncertainty in LAI and species composition caused errors in net canopy assimilation of as much as 42–70% and 14–36%, respectively, depending on forest type. The sensitivity of net canopy assimilation to species-dependent physiology was greater between needleleaf coniferous and broadleaf deciduous life forms than among species within life forms. A simple regression model that recognized stand differences in LAI and life-form type (needleleaf coniferous, broadleaf deciduous) accounted for 94% of the variation in simulated net canopy assimilation for the 21 stands. Expanding the model to include species composition rather than life-form type only accounted for an additional 1% of the variation in simulated net canopy assimilation. The statistical model was applied to a synthetic aperture radar scene that discriminatted black spruce, white spruce, balsam poplar, and alder forests in a 76.8 km2 region near Fairbanks. This analysis showed that regional net canopy assimilation was as sensitive to the area of each forest type as it was to the LAI of each forest type. The analyses reported in this article highlight the importance of recognizing physiological differences among needleleaf coniferous and broadleaf deciduous forest types when estimating regional net canopy assimilation in boreal forests.

Abstract Urbanization, the expansion of built-up areas, is an important yet less-studied aspect of land use/land cover change in climate science. To date, most global climate models used to evaluate effects of land use/land cover change on climate do not include an urban parameterization. Here, the authors describe the formulation and evaluation of a parameterization of urban areas that is incorporated into the Community Land Model, the land surface component of the Community Climate System Model. The model is designed to be simple enough to be compatible with structural and computational constraints of a land surface model coupled to a global climate model yet complex enough to explore physically based processes known to be important in determining urban climatology. The city representation is based upon the “urban canyon” concept, which consists of roofs, sunlit and shaded walls, and canyon floor. The canyon floor is divided into pervious (e.g., residential lawns, parks) and impervious (e.g., roads, parking lots, sidewalks) fractions. Trapping of longwave radiation by canyon surfaces and solar radiation absorption and reflection is determined by accounting for multiple reflections. Separate energy balances and surface temperatures are determined for each canyon facet. A one-dimensional heat conduction equation is solved numerically for a 10-layer column to determine conduction fluxes into and out of canyon surfaces. Model performance is evaluated against measured fluxes and temperatures from two urban sites. Results indicate the model does a reasonable job of simulating the energy balance of cities.

A land surface model that includes a subgrid parameterization for inland water (lake, swamp, marsh) was coupled to a modified version of the NCAR CCM2. The coupled model was run for 5 yr with and without inland water subgrid points to determine the importance of inland water for global climate simulation. In July, the inclusion of these water bodies resulted in a spatially consistent signal in which high inland water regions were 2°–3°C cooler, had increased latent heat flux (10–45 W m−2), and decreased sensible heat flux (5–30 W m−2) compared to the simulation without these water bodies. These changes were statistically significant in the lake region of northwest Canada, the Great Lakes region of North America, the swamp and marsh region of the Siberian lowlands, and the lake region of East Africa, but were not significantly different in the swamp and marsh region of Finland and northwest Russia. The effect on Northern Hemisphere January air temperature was difficult to interpret due to large interannual variability. In tropical lake regions (East Africa), the response to lakes was less in the rainy season (January) than in the dry season (July). Precipitation was unchanged in both months except for the Great Lakes region where precipitation increased in January. These changes in temperature, precipitation, and surface fluxes were consistent with mesoscale modeling studies of the effects of lakes on climate and tended to bring the model closer to observations. In particular, the summer cooling in North America helped reduce a large warm temperature bias in the model, but did not eliminate the bias. The lakes had little effect on atmospheric moisture, radiation, or zonal circulation. These results show that subgrid-scale inland water bodies can be successfully added to global land surface models for use with GCMS.

Mosses dominate the surface cover in high northern latitudes and have the potential to play a key role in modifying the thermal and hydrologic regime of Arctic soils. These modifications in turn feed back to influence surface energy exchanges and hence may affect regional climate. However, mosses are poorly represented in models of the land surface. In this study the NCAR Land Surface Model (LSM) was modified in two ways. First, additional soil texture types including mosses and lichens were added to more realistically represent northern soils. Second, the LSM was also modified so that a different soil texture type could be specified for each layer. Several experiments were performed using climate data from an Arctic tundra site in 1995. The model was run for a homogeneous loam soil column and then also for columns that included moss, lichen, peat, and sand. The addition of a surface layer of moss underlain by peat and loam had a substantial impact on modeled surface processes. First, moss acted as an insulative layer producing cooler summer temperatures (6.9°C lower at 0.5 m) and warmer winter temperatures (2.3°C higher at 0.5 m) when compared with a homogenous loam soil column. Second, a soil column with a moss surface had a greater surface infiltration, leading to greater storage of soil moisture in lower layers when compared with a homogeneous loam column. Last, moss modulated the surface energy exchanges by decreasing soil heat flux (57% in July) and increasing turbulent fluxes of heat (67% in July) and moisture (15% in July). Mosses were also more effective contributors to total latent heating than was a bare loam surface. These results suggest that the addition of moss and the ability to prescribe different soil textures for different soil layers result in a more plausible distribution of heat and water within the column and that these modifications should be incorporated into regional and global climate models.

Recent developments in individual-based forest simulators have made it possible to extend the basic approach to a wider range of forest ecosystems. One recent trend is toward more general representations of abiotic processes, and more attention to the role of tree life-history traits in generating forest response to environmental gradients. Gap models that explicitly attend spatial aspects of the light regime can be extended to simulate forest pattern at scales larger than the forest gap; examples at landscape and geographic scales are presented.

Increasing the albedo of urban surfaces has received attention as a strategy to mitigate urban heat islands. Here, the effects of globally installing white roofs are assessed using an urban canyon model coupled to a global climate model. Averaged over all urban areas, the annual mean heat island decreased by 33%. Urban daily maximum temperature decreased by 0.6°C and daily minimum temperature by 0.3°C. Spatial variability in the heat island response is caused by changes in absorbed solar radiation and specification of roof thermal admittance. At high latitudes in winter, the increase in roof albedo is less effective at reducing the heat island due to low incoming solar radiation, the high albedo of snow intercepted by roofs, and an increase in space heating that compensates for reduced solar heating. Global space heating increased more than air conditioning decreased, suggesting that end‐use energy costs must be considered in evaluating the benefits of white roofs.

Recent studies indicate that nitrogen biogeochemistry affects the carbon cycle feedback in climate simulations. We use the Community Land Model version 4 (CLM4) with carbon‐only and carbon‐nitrogen biogeochemistry to assess the influence of nitrogen on the land carbon budget for 1973–2004. Carbon‐only simulations show that the carbon gain from increasing atmospheric CO 2 (the concentration‐carbon feedback) is four times greater than the warming‐induced carbon loss (the climate‐carbon feedback) over the period 1973–2004. Nitrogen reduces both feedbacks compared with carbon‐only biogeochemistry. The decrease in the concentration‐carbon feedback is three times greater than the effect on the climate‐carbon feedback. Thus, the influence of nitrogen on the CLM4 concentration‐carbon feedback is of greater importance for near‐term climate change simulations than its effect on the climate‐carbon feedback. Furthermore, the land use carbon flux greatly exceeds these carbon‐nitrogen biogeochemical feedbacks.

Boreal forests contain large quantities of soil carbon, prompting concern that climatic warming may stimulate decomposition and accentuate increasing atmospheric CO 2 concentrations. While soil warming increases decomposition rates, the accompanying increase in nutrient mineralization may promote tree growth in these nutrient-poor soils and thereby compensate for the increased carbon loss during decomposition. We used a model of production and decomposition to test this hypothesis. In black spruce (Piceamariana (Mill.) B.S.P.), white spruce (Piceaglauca (Moench) Voss), and paper birch (Betulapapyrifera Marsh.) forests, decomposition increased with the soil warming caused by a 5 °C increase in air temperature. However, increased nitrogen mineralization promoted tree growth, offsetting the increased carbon loss during decomposition. In the black spruce forest, increased tree production was maintained for the 25 years of simulation. Whether this can be maintained indefinitely is unknown. In the birch forest, tree production decreased to prewarming levels after about 10 years. Our analyses examined only the consequences of belowground feedbacks that affect ecosystem carbon uptake with climatic warming. These analyses highlight the importance of interactions among net primary production, decomposition, and nitrogen mineralization in determining the response of forest ecosystems to climatic change.

The Community Climate System Model (CCSM) has been created to represent the principal components of the climate system and their interactions. Development and applications of the model are carried out by the U.S. climate research community, thus taking advantage of both wide intellectual participation and computing capabilities beyond those available to most individual U.S. institutions. This article outlines the history of the CCSM, its current capabilities, and plans for its future development and applications, with the goal of providing a summary useful to present and future users. The initial version of the CCSM included atmosphere and ocean general circulation models, a land surface model that was grafted onto the atmosphere model, a sea-ice model, and a “flux coupler” that facilitates information exchanges among the component models with their differing grids. This version of the model produced a successful 300-yr simulation of the current climate without artificial flux adjustments. The model was then used to perform a coupled simulation in which the atmospheric CO2 concentration increased by 1 % per year. In this version of the coupled model, the ocean salinity and deep-ocean temperature slowly drifted away from observed values. A subsequent correction to the roughness length used for sea ice significantly reduced these errors. An updated version of the CCSM was used to perform three simulations of the twentieth century's climate, and several projections of the climate of the twenty-first century. The CCSM's simulation of the tropical ocean circulation has been significantly improved by reducing the background vertical diffusivity and incorporating an anisotropic horizontal viscosity tensor. The meridional resolution of the ocean model was also refined near the equator. These changes have resulted in a greatly improved simulation of both the Pacific equatorial undercurrent and the surface countercurrents. The interannual variability of the sea surface temperature in the central and eastern tropical Pacific is also more realistic in simulations with the updated model. Scientific challenges to be addressed with future versions of the CCSM include realistic simulation of the whole atmosphere, including the middle and upper atmosphere, as well as the troposphere; simulation of changes in the chemical composition of the atmosphere through the incorporation of an integrated chemistry model; inclusion of global, prognostic biogeochemical components for land, ocean, and atmosphere; simulations of past climates, including times of extensive continental glaciation as well as times with little or no ice; studies of natural climate variability on seasonal-to-centennial timescales; and investigations of anthropogenic climate change. In order to make such studies possible, work is under way to improve all components of the model. Plans call for a new version of the CCSM to be released in 2002. Planned studies with the CCSM will require much more computer power than is currently available.

Abstract Decomposition is a large term in the global carbon budget, but models of the earth system that simulate carbon cycle‐climate feedbacks are largely untested with respect to litter decomposition. We tested the litter decomposition parameterization of the community land model version 4 ( CLM 4), the terrestrial component of the community earth system model, with data from the long‐term intersite decomposition experiment team ( LIDET ). The LIDET dataset is a 10‐year study of litter decomposition at multiple sites across North America and Central America. We performed 10‐year litter decomposition simulations comparable with LIDET for 9 litter types and 20 sites in tundra, grassland, and boreal, conifer, deciduous, and tropical forest biomes using the LIDET ‐provided climatic decomposition index to constrain temperature and moisture effects on decomposition. We performed additional simulations with DAYCENT , a version of the CENTURY model, to ask how well an established ecosystem model matches the observations. The results show large discrepancy between the laboratory microcosm studies used to parameterize the CLM 4 litter decomposition and the LIDET field study. Simulated carbon loss is more rapid than the observations across all sites, and nitrogen immobilization is biased high. Closer agreement with the observations requires much lower decomposition rates, obtained with the assumption that soil mineral nitrogen severely limits decomposition. DAYCENT better replicates the observations, for both carbon mass remaining and nitrogen, independent of nitrogen limitation. CLM 4 has low soil carbon in global earth system simulations. These results suggest that this bias arises, in part, from too rapid litter decomposition. More broadly, the terrestrial biogeochemistry of earth system models must be critically tested with observations, and the consequences of particular model choices must be documented. Long‐term litter decomposition experiments such as LIDET provide a real‐world process‐oriented benchmark to evaluate models.

The Community Land Model version 4 overestimates gross primary production (GPP) compared with estimates from FLUXNET eddy covariance towers. The revised model of Bonan et al. (2011) is consistent with FLUXNET, but values for the leaf‐level photosynthetic parameter V c max that yield realistic GPP at the canopy‐scale are lower than observed in the global synthesis of Kattge et al. (2009), except for tropical broadleaf evergreen trees. We investigate this discrepancy between V c max and canopy fluxes. A multilayer model with explicit calculation of light absorption and photosynthesis for sunlit and shaded leaves at depths in the canopy gives insight to the scale mismatch between leaf and canopy. We evaluate the model with light‐response curves at individual FLUXNET towers and with empirically upscaled annual GPP. Biases in the multilayer canopy with observed V c max are similar, or improved, compared with the standard two‐leaf canopy and its low V c max , though the Amazon is an exception. The difference relates to light absorption by shaded leaves in the two‐leaf canopy, and resulting higher photosynthesis when the canopy scaling parameter K n is low, but observationally constrained. Larger K n decreases shaded leaf photosynthesis and reduces the difference between the two‐leaf and multilayer canopies. The low model V c max is diagnosed from nitrogen reduction of GPP in simulations with carbon‐nitrogen biogeochemistry. Our results show that the imposed nitrogen reduction compensates for deficiency in the two‐leaf canopy that produces high GPP. Leaf trait databases ( V c max ), within‐canopy profiles of photosynthetic capacity ( K n ), tower fluxes, and empirically upscaled fields provide important complementary information for model evaluation.

Our current understanding of the ecology of boreal forests indicates that vegetation patterns within the circumpolar boreal forest reflect a complex interrelationship among climate, solar radiation, soil moisture, soil temperature, the forest floor organic layer, forest fires, and insect outbreaks. In this paper, a simulation model was used to explore the environmental subset of this interrelationship, specifically the interactions among solar radiation, soil moisture, soil freezing and thawing, the forest floor organic layer, and forest fires. The model solved for these environmental factors on a monthly time step using easily obtainable soils and climatic data. The algorithms developed in this study successfully reproduced local, seasonal patterns of solar radiation, soil moisture, and depths of freeze and thaw for different topographies at Fairbanks, Alaska. These same algorithms also reproduced regional patterns of the annual solar radiation, soil moisture, and soil thermal regimes in boreal forests of North America, Scandinavia, and the Soviet Union. By calculating these environmental factors on a monthly time step, these algorithms operate at a temporal scale suitable for individual-tree models of forest succession, which can be used to study extent vegetation patterns in boreal forests and the possible ecological consequences of climatic changes.

Abstract Version 1 of the Community Earth System Model, in the configuration where its full carbon cycle is enabled, is introduced and documented. In this configuration, the terrestrial biogeochemical model, which includes carbon–nitrogen dynamics and is present in earlier model versions, is coupled to an ocean biogeochemical model and atmospheric CO2 tracers. The authors provide a description of the model, detail how preindustrial-control and twentieth-century experiments were initialized and forced, and examine the behavior of the carbon cycle in those experiments. They examine how sea- and land-to-air CO2 fluxes contribute to the increase of atmospheric CO2 in the twentieth century, analyze how atmospheric CO2 and its surface fluxes vary on interannual time scales, including how they respond to ENSO, and describe the seasonal cycle of atmospheric CO2 and its surface fluxes. While the model broadly reproduces observed aspects of the carbon cycle, there are several notable biases, including having too large of an increase in atmospheric CO2 over the twentieth century and too small of a seasonal cycle of atmospheric CO2 in the Northern Hemisphere. The biases are related to a weak response of the carbon cycle to climatic variations on interannual and seasonal time scales and to twentieth-century anthropogenic forcings, including rising CO2, land-use change, and atmospheric deposition of nitrogen.

Abstract To help understand potential effects of urbanization on climates of varying scales and effects of climate change on urban populations, urbanization must be included in global climate models (GCMs). To properly capture the spatial variability in urban areas, GCMs require global databases of urban extent and characteristics. This article describes methods and characteristics used to create a data set that can be utilized to simulate urban systems on a global scale within GCMs. The data set represents three main categories of urban properties: spatial extent, urban morphology, and thermal and radiative properties of building materials. Spatial extent of urban areas is derived from a population density data set and calibrated within thirty-three regions of similar physical and social characteristics. For each region, four classes of urbanization are identified and linked to a set of typical building morphology, thermal, and radiative characteristics. In addition, urban extent is simulated back in time to 1750 based on national historical population and urbanization trends. A sample set of simulations shows that the urban characteristics do change urban heat island outcomes. In general the simulations show greater urban heat islands with increasing latitude, in agreement with observations. [Supplemental material is available for this article. Go to the publisher's online edition of Annals of the Association of American Geographers for the following free supplemental resource: (1) a table of the Global Data Set of Urban and Building Properties © 2007–2009.] Para ayudar a comprender los efectos potenciales de la urbanización sobre los climas a escalas variables y los efectos del cambio climático sobre las poblaciones urbanas, tenemos que incorporar la urbanización en los modelos del clima global (GCMs, sigla en inglés). Para que la variabilidad espacial en áreas urbanas se capte adecuadamente, los GCMs deben contar con bases de datos globales sobre la extensión y características urbanas. Este artículo describe los métodos y características usados para crear un conjunto de datos susceptible de utilizarse en los GCMs para la simulación de sistemas urbanos a escala global. El conjunto de datos representa las tres principales categorías de las propiedades de lo urbano: extensión espacial, morfología urbana y propiedades térmicas y radiantes de los materiales con los que está construida la ciudad. La extensión espacial de las áreas urbanas se derivó desde un conjunto de datos de densidad de población y se calibró al interior de treinta y tres regiones de de características físicas y sociales similares. Para cada región se identificaron cuatro clases de urbanización y éstas fueron relacionadas con un conjunto de típicas características termales, radiantes y morfológicas de las edificaciones. Por otra parte, se hizo una proyección de la extensión urbana hacia el pasado para simular las condiciones de 1750, con base en las tendencias históricas nacionales de población y urbanización. Un conjunto de muestras de las simulaciones indica que las características urbanas en verdad cambian la magnitud de las islas de calor urbano. En general, a mayor latitud las simulaciones muestran mayores islas de calor urbano, lo cual concuerda con las observaciones. [Hay disponible material suplementario para este artículo. Ir a la edición electrónica del publicista de Annals of the Association of American Geographers por el siguiente recurso suplementario gratuito: (1) una tabla del Conjunto de Datos Globales de las Propiedades Urbanas y de las Edificaciones © 2007–2009.] Key Words: climate simulationglobal climate changeurban climateurban properties关键词: 气候模拟全球气候变化城市气候城市特性Palabras clave: simulación climáticacambio climático globalclima urbanopropiedades de lo urbano Acknowledgments This research was partly supported by the National Center for Atmospheric Research Weather and Climate Impact Assessment Program and the Water System Program, National Science Foundation Grants ATM-0107404 and ATM-0413540, and the University of Kansas, Center for Research. The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Abstract The Community Earth System Model, version 1 (CESM1) is evaluated with two coupled atmosphere–land simulations. The CTRL (control) simulation represents crops as unmanaged grasses, while CROP represents a crop managed simulation that includes special algorithms for midlatitude corn, soybean, and cereal phenology and carbon allocation. CROP has a more realistic leaf area index (LAI) for crops than CTRL. CROP reduces winter LAI and represents the spring planting and fall harvest explicitly. At the peak of the growing season, CROP simulates higher crop LAI. These changes generally reduce the latent heat flux but not around peak LAI (late spring/early summer). In midwestern North America, where corn, soybean, and cereal abundance is high, simulated peak summer precipitation declines and agrees better with observations, particularly when crops emerge late as is found from a late planting sensitivity simulation (LateP). Differences between the CROP and LateP simulations underscore the importance of simulating crop planting and harvest dates correctly. On the biogeochemistry side, the annual cycle of net ecosystem exchange (NEE) also improves in CROP relative to Ameriflux site observations. For a global perspective, the authors diagnose annual cycles of CO2 from the simulated NEE (CO2 is not prognostic in these simulations) and compare against representative GLOBALVIEW monitoring stations. The authors find an increased (thus also improved) amplitude of the annual cycle in CROP. These regional and global-scale refinements from improvements in the simulated plant phenology have promising implications for the development of the CESM and particularly for simulations with prognostic atmospheric CO2.

Abstract A parameterization for urban surfaces has been incorporated into the Community Land Model as part of the Community Climate System Model. The parameterization allows global simulation of the urban environment, in particular the temperature of cities and thus the urban heat island. Here, the results from climate simulations for the AR4 A2 emissions scenario are presented. Present‐day annual mean urban air temperatures are up to 4 °C warmer than surrounding rural areas. Averaged over all urban areas resolved in the model, the heat island is 1.1 °C, which is 46% of the simulated mid‐century warming over global land due to greenhouse gases. Heat islands are generally largest at night as evidenced by a larger urban warming in minimum than maximum temperature, resulting in a smaller diurnal temperature range compared to rural areas. Spatial and seasonal variability in the heat island is caused by urban to rural contrasts in energy balance and the different responses of these surfaces to the seasonal cycle of climate. Under simulation constraints of no urban growth and identical urban/rural atmospheric forcing, the urban to rural contrast decreases slightly by the end of the century. This is primarily a different response of rural and urban areas to increased long‐wave radiation from a warmer atmosphere. The larger storage capacity of urban areas buffers the increase in long‐wave radiation such that urban night‐time temperatures warm less than rural. Space heating and air conditioning processes add about 0.01 W m −2 of heat distributed globally, which results in a small increase in the heat island. The significant differences between urban and rural surfaces demonstrated here imply that climate models need to account for urban surfaces to more realistically evaluate the impact of climate change on people in the environment where they live. Copyright © 2010 Royal Meteorological Society

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

Abstract Ozone (O3) is a phytotoxic greenhouse gas that has increased more than threefold at Earth’s surface from preindustrial values. In addition to directly increasing radiative forcing as a greenhouse gas, O3 indirectly impacts climate through altering the plant processes of photosynthesis and transpiration. While global estimates of gross primary productivity (GPP) have incorporated the effects of O3, few studies have explicitly determined the independent effects of O3 on transpiration. In this study, the authors include effects of O3 on photosynthesis and stomatal conductance from a recent literature review to determine the impact on GPP and transpiration and highlight uncertainty in modeling plant responses to O3. Using the Community Land Model, the authors estimate that present-day O3 exposure reduces GPP and transpiration globally by 8%–12% and 2%–2.4%, respectively. The largest reductions were in midlatitudes, with GPP decreasing up to 20% in the eastern United States, Europe, and Southeast Asia and transpiration reductions of up to 15% in the same regions. Larger reductions in GPP compared to transpiration decreased water-use efficiency 5%–10% in the eastern United States, Southeast Asia, Europe, and central Africa; increased surface runoff more than 15% in eastern North America; and altered patterns of energy fluxes in the tropics, high latitudes, and eastern North America. Future climate predictions will be improved if plant responses to O3 are incorporated into models such that stomatal conductance is modified independently of photosynthesis and the effects on transpiration are explicitly considered in surface energy budgets. Improvements will help inform regional decisions for managing changes in hydrology and surface temperatures in response to O3 pollution.

Simulating global fluxes of water, carbon, and energy at the land surface requires accurate and versatile models of stomatal conductance, currently represented by structurally similar and interchangeable forms that share weaknesses at environmental extremes. ### Glossary ABA : abscisic acid BB : Ball-Berry MED : Medlyn RuBP : ribulose 1,5-bisphosphate CLM : community land model WUE : water-use efficiency optimization * ### Glossary ABA : abscisic acid BB : Ball-Berry MED : Medlyn RuBP : ribulose 1,5-bisphosphate CLM : community land model WUE : water-use efficiency optimization

Uncertainties in terrestrial carbon (C) cycle projections increase uncertainty of potential climate feedbacks. Efforts to improve model performance often include increased representation of biogeochemical processes, such as coupled carbon–nitrogen (N) cycles. In doing so, models are becoming more complex, generating structural uncertainties in model form that reflect incomplete knowledge of how to represent underlying processes. Here, we explore structural uncertainties associated with biological nitrogen fixation (BNF) and quantify their effects on C cycle projections. We find that alternative plausible structures to represent BNF result in nearly equivalent terrestrial C fluxes and pools through the twentieth century, but the strength of the terrestrial C sink varies by nearly a third (50 Pg C) by the end of the twenty-first century under a business-as-usual climate change scenario representative concentration pathway 8.5. These results indicate that actual uncertainty in future C cycle projections may be larger than previously estimated, and this uncertainty will limit C cycle projections until model structures can be evaluated and refined.

Land cover and land use influence surface climate through differences in biophysical surface properties, including partitioning of sensible and latent heat (e.g., Bowen ratio), surface roughness, and albedo. Clusters of closely spaced eddy covariance towers (e.g., <10 km) over a variety of land cover and land use types provide a unique opportunity to study the local effects of land cover and land use on surface temperature. We assess contributions albedo, energy redistribution due to differences in surface roughness and energy redistribution due to differences in the Bowen ratio using two eddy covariance tower clusters and the coupled (land-atmosphere) Variable-Resolution Community Earth System Model. Results suggest that surface roughness is the dominant biophysical factor contributing to differences in surface temperature between forested and deforested lands. Surface temperature of open land is cooler (−4.8 °C to −0.05 °C) than forest at night and warmer (+0.16 °C to +8.2 °C) during the day at northern and southern tower clusters throughout the year, consistent with modeled calculations. At annual timescales, the biophysical contributions of albedo and Bowen ratio have a negligible impact on surface temperature, however the higher albedo of snow-covered open land compared to forest leads to cooler winter surface temperatures over open lands (−0.4 °C to −0.8 °C). In both the models and observation, the difference in mid-day surface temperature calculated from the sum of the individual biophysical factors is greater than the difference in surface temperature calculated from radiative temperature and potential temperature. Differences in measured and modeled air temperature at the blending height, assumptions about independence of biophysical factors, and model biases in surface energy fluxes may contribute to daytime biases.

Abstract. Plants have a strong influence on climate by controlling the transfer of carbon dioxide and water between the biosphere and atmosphere during the processes of photosynthesis and transpiration. Chronic exposure to surface ozone (O3) differentially affects photosynthesis and transpiration because it damages stomatal conductance, the common link that controls both processes, in addition to the leaf biochemistry that only affects photosynthesis. Because of the integral role of O3 in altering plant interactions with the atmosphere, there is a strong motivation to incorporate the influence of O3 into regional and global models. However, there are currently no analyses documenting both photosynthesis and stomatal conductance responses to O3 exposure through time using a standardized O3 parameter that can be easily incorporated into models. Therefore, models often rely on photosynthesis data derived from the responses of one or a few plant species that exhibit strong negative correlations with O3 exposure to drive both rates of photosynthesis and transpiration, neglecting potential divergence between the two fluxes. Using data from the peer-reviewed literature, we have compiled photosynthetic and stomatal responses to chronic O3 exposure for all plant types with data available in the peer-reviewed literature as a standardized function of cumulative uptake of O3 (CUO), which integrates O3 flux into leaves through time. These data suggest that stomatal conductance decreases ~11% after chronic O3 exposure, while photosynthesis independently decreases ~21%. Despite the overall decrease in both variables, high variance masked any correlations between the decline in photosynthesis or stomatal conductance with increases in CUO. Though correlations with CUO are not easily generalized, existing correlations demonstrate that photosynthesis tends to be weakly but negatively correlated with CUO while stomatal conductance is more often positively correlated with CUO. Results suggest that large-scale models using data with strong negative correlations that only affect photosynthesis need to reconsider the generality of their response. Data from this analysis are now available to the scientific community and can be incorporated into global models to improve estimates of photosynthesis, global land-carbon sinks, hydrology, and indirect radiative forcing that are influenced by chronic O3 exposure.

Abstract Soils contain large reservoirs of terrestrial carbon (C), yet soil C dynamics simulated in Earth systems models show little agreement with each other or with observational data sets. This uncertainty underscores the need to develop a framework to more thoroughly evaluate model parameterizations, structures, and projections. Toward this end we used an analytical solution to calculate approximate equilibrium soil C pools for the Community Land Model version 4 (CLM4cn) and Daily Century (DAYCENT) soil biogeochemistry models. Neither model generated sufficient soil C pools when forced with litterfall inputs from CLM4cn; however, global totals and spatial correlations of soil C pools for both models improved when calculated with litterfall inputs derived from observational data. DAYCENT required additional modifications to simulate soil C pools in deeper soils (0–100 cm). Our best simulations produced global soil C pools totaling 746 and 978 Pg C for CLM4cn and DAYCENT parameterizations, respectively, compared to observational estimates of 1259 Pg C. In spite of their differences in complexity and equilibrium soil C pools, predictions of soil C losses with warming temperatures through 2100 were strikingly similar for both models. Ultimately, CLM4cn and DAYCENT come from the same class of models that represent the turnover of soil C as a first‐order decay process. While this approach may have utility in calculating steady state soil C pools, the applicability of this model configuration in transient simulations remains poorly evaluated.

Carbon uptake by the oceans and terrestrial biosphere regulates atmospheric carbon dioxide concentration and affects Earth's climate, yet global carbon cycle projections over the next century are highly uncertain. Here, we quantify and isolate the sources of projection uncertainty in cumulative ocean and terrestrial carbon uptake over 2006–2100 by performing an analysis of variance on output from an ensemble of 12 Earth System Models. Whereas uncertainty in projections of global ocean carbon accumulation by 2100 is <100 Pg C and driven primarily by emission scenario, uncertainty in projections of global terrestrial carbon accumulation by 2100 is >160 Pg C and driven primarily by model structure. To statistically reduce uncertainty in terrestrial carbon projections, we devise schemes to weight the models based on their ability to represent the observed change in carbon accumulation over 1959–2005. The weighting schemes incrementally reduce uncertainty to a minimum value of 125 Pg C in 2100, but this reduction requires an impractical observational constraint. We suggest that a focus on reducing multi-model spread may not make terrestrial carbon cycle projections more reliable, and instead advocate for accurate observations, improved process understanding, and a multitude of modeling approaches.

Climate models have evolved into Earth system models with representation of the physics, chemistry, and biology of terrestrial ecosystems. This companion book to Gordon Bonan's Ecological Climatology: Concepts and Applications, Third Edition, builds on the concepts introduced there, and provides the mathematical foundation upon which to develop and understand ecosystem models and their relevance for these Earth system models. The book bridges the disciplinary gap among land surface models developed by atmospheric scientists; biogeochemical models, dynamic global vegetation models, and ecosystem demography models developed by ecologists; and ecohydrology models developed by hydrologists. Review questions, supplemental code, and modeling projects are provided, to aid with understanding how the equations are used. The book is an invaluable guide to climate change and terrestrial ecosystem modeling for graduate students and researchers in climate change, climatology, ecology, hydrology, biogeochemistry, meteorology, environmental science, mathematical modeling, and environmental biophysics.

Climate model simulations have shown that conversion of natural forest vegetation to croplands in the United States cooled climate. The cooling was greater for daily maximum temperature than for daily minimum temperature, resulting in a reduced diurnal temperature range. This paper presents analyses of observed daily maximum and minimum temperatures that are consistent with the climate simulations. Daily maximum temperature in the croplands of the Midwest United States is reduced relative to forested land in the Northeast, resulting in a decreased diurnal temperature range. The cooling is regional rather than local and is likely created by the contrast between extensive cropland in the Midwest and forest in the Northeast. Seasonal patterns of this cooling are correlated with seasonal changes in crop growth. Analyses of historical temperatures since 1900 and reconstructed cropland extent show a temporal correlation between land use and cooling. The cooling created by the forest–cropland contrast is much more prominent now, when much of the Northeast farmland has been abandoned and reforested, than in the early 1900s when farmlands were more extensive in the Northeast. These results show that human uses of land, especially clearing of forest for agriculture and reforestation of abandoned farmland, are an important cause of regional climate change. Analyses of historical temperature records must consider this “land use” forcing.

The modern vegetated landscape of the United States little resembles the “natural” landscape prior to colonial settlement. Two 10-yr climate simulations with a global climate model using natural and modern vegetation maps for land surface boundary conditions showed that the conversion of forest to cropland in the eastern and central United States cooled climate. Mean annual surface air temperature decreased by 0.6°–1.0°C in the United States east of 100° W. The decrease in daily maximum temperature exceeded that for daily minimum temperature for a decreased diurnal temperature range of about 0.6°C, averaged over the year. The cooling was greatest in the Midwest in summer and autumn, when daily mean temperature decreased by >0.5° and 2.5°C, respectively; daily maximum temperature decreased by 1°–3°C; and diurnal temperature range decreased by about 1°C. U.S. temperature records show that much of the 1800s was anomalously cold, particularly in summer and autumn in the Midwest. Although this was likely caused by natural climate variability, increased volcanic activity, and decreased solar activity, the clearing of forests for agricultural land undoubtedly contributed to the cold temperatures in the eastern and central Unites States.

The biogeographical distribution of different vegetation types and the physiological status of vegetation are significant controls of energy, water, and C02 exchanges between land surfaces and the atmosphere. These exchanges affect local, regional, and global climates, which feedback to affect the biogeography and physiology of the vegetation. Consequently, there is interest in developing a comprehensive land surface scheme that integrates bio-physical, biogeochernical, and ecosystem dynamical processes. Land surface process models of energy and moisture exchanges, ecosystem biogeochemistry models, and ecosystem dynamics models share many features. However, these models have been developed independently by groups interested in their respective field not in the integration across fields. Thus, there are important discrepancies among models that will have to be reconciled if an integrative land-atmosphere interaction package is to be developed. In particular, the temporal resolution and biophysical rigor of land surface process models and the links among energy, water, and C02 exchange make these models the logical model to calculate land-atmosphere CO2 exchange at diurnal to annual time scales. Longer-term exchanges can be simulated by including plant demography and nutrient cycling. Models that combine the biophysical and biogeochemical controls of C02 exchange help define remote sensing applications important to modeling the seasonal and annual carbon balance of terrestrial ecosystems. Sensitivity analyses with such a model show that the annual production of biomass and the seasonal cycle of C02 exchange in boreal forests are well approximated merely by knowing the beginning and end of the growing season, absorbed photosynthetically active radiation, foliage nitrogen concentration, and vegetation type.

Abstract The effects of increased soil moisture on wet season (October–March) precipitation in southern Africa are investigated using the Community Climate System Model version 3 (CCSM3). In the CTRL case, soil moisture is allowed to interact dynamically with the atmosphere. In the MOIST case, soil moisture is set so that evapotranspiration is not limited by the supply of water. The MOIST scenario actually results in decreased precipitation over the region of perturbed soil moisture, compared to CTRL. The increased soil moisture alters the surface energy balance, resulting in a shift from sensible to latent heating. This manifests in two ways relevant for precipitation processes. First, the shift from sensible to latent heating cools the surface, causing a higher surface pressure, a reduced boundary layer height, and an increased vertical gradient in equivalent potential temperature. These changes are indicative of an increase in atmospheric stability, inhibiting vertical movement of air parcels and decreasing the ability of precipitation to form. Second, the surface changes induce anomalous surface divergence and increased subsidence. This causes a reduction in cloud cover and specific humidity above 700 hPa and results in a net decrease of column-integrated precipitable water, despite the increased surface water flux, indicating a reduction in moisture convergence. Based on this and a previous study, soil moisture may act as a negative feedback to precipitation in southern Africa, helping to buffer the system against any external forcing of precipitation (e.g., ENSO).

Observed soil degree‐days (SDD) for 20 forest stands in the discontinuous permafrost zone of interior Alaska range from 483 to 2217. These stands differ in terms of forest structure, topography, and soils. A biophysical model that solves the surface energy budget of a multilayer forest canopy was used to examine which site factors were most important in controlling the observed soil temperature gradient. Simulated soil temperature averaged 851 SDD for the 20 sites. Sensitivity analyses indicated that this average could vary by 0–88 SDD (0–10% of the mean) because of possible parameter error. Removing the forest canopy and the moss cover caused the soil to warm, on average, by 408 and 345 SDD, respectively. Elevation and soil drainage differences among sites were of secondary importance, causing SDD to deviate by 71 and 66 SDD, respectively. Slope and aspect had little effect on soil temperature.

This chapter is a review on the modeling of the potential response of vegetation to global climate change.. Models provide a means of formalizing a set of assumptions/hypotheses linking pattern and process, allowing for extrapolation beyond the range of observed phenomena. The purpose of this chapter is not to provide an exhaustive review of models relating climate and plant pattern; rather it is to examine a specific set of models which are currently being used to investigate the question of plant response to climate change at a global scale. The focus is on developing a methodology for predicting changes in the large-scale distribution of vegetation (that is, global distribution of biomes or ecosystem complexes) under changing global climate patterns. This chapter starts with a discussion on climate–vegetation classification. The chapter focuses on the application of holdridge life-zone classification to climate change at a global scale, followed by the application of a plant energy balance model to predicting changes in leaf area under changing climate conditions. This is followed by the description of modeling temporal dynamics. Furthermore, this chapter introduces individual-based forest gap models. The chapter ends with the discussion of application of gap models to predict forest response to climate change.

Abstract The Community Land Model version 3 (CLM3) Dynamic Global Vegetation Model (CLM–DGVM) is used diagnostically to identify land and atmospheric model biases that lead to biases in the simulated vegetation. The CLM–DGVM driven with observed atmospheric data (offline simulation) underestimates global forest cover, overestimates grasslands, and underestimates global net primary production. These results are consistent with earlier findings that the soils in CLM3 are too dry. In the offline simulation an increase in simulated transpiration by changing this variable's soil moisture dependence and by decreasing canopy-intercepted precipitation results in better global plant biogeography and global net primary production. When CLM–DGVM is coupled to the Community Atmosphere Model version 3 (CAM3), the same modifications do not improve simulated vegetation in the eastern United States and Amazonia where the most serious vegetation biases appear. The dry bias in eastern U.S. precipitation is so severe that the simulated vegetation is insensitive to changes in the hydrologic cycle. In Amazonia, strong coupling among soil moisture, vegetation, evapotranspiration, and precipitation produces a highly complex hydrologic cycle in which small perturbations in precipitation are accentuated by vegetation. These interactions in Amazonia lead to a dramatic precipitation decrease and a collapse of the forest. These results suggest that the accurate parameterization of convection poses a complex and challenging scientific issue for climate models that include dynamic vegetation. The results also emphasize the difficulties that may arise when coupling any two highly nonlinear systems that have only been tested uncoupled.

The statistical representation of multiple land surfaces within a grid cell has received attention as a means to parameterize the nonlinear effects of subgrid-scale heterogeneity on land-atmosphere energy exchange. However, previous analyses have not identified the critical land-surface parameters to which energy exchanges are sensitive; the appropriate number of within-grid-cell classes for a particular parameter, or the effects of interactions among several parameters on the nonlinearity of energy exchanges. The analyses reported here used a land-surface scheme for climate models to examine the effects of subgrid variability in leaf area index, minimum and maximum stomatal resistances, and soil moisture on grid-scale fluxes. Comparisons between energy fluxes obtained using parameter values for the average of 100 subgrid points and the average fluxes for the 100 subgrid points showed minor differences for emitted infrared radiation and reflected solar radiation, but large differences for sensible heat and evapotranspiration. Leaf area index was the most important parameter; stomatal resistances were only important on wet soils. Interactions among parameters increased the nonlinearity of land-atmosphere energy exchange. When considered separately, six to ten values of each parameter greatly reduced the deviation between the two flux estimates. However, this approach became cumbersome when all four parameters varied independently. These analyses suggest that the debate over how to best parameterize the nonlinear effects of subgrid-scale heterogeneity on land-atmosphere interactions will continue.

The Community Climate System Model (CCSM) calculates terrestrial biogenic volatile organic compound (BVOC) emissions using an algorithm developed from field and laboratory observations. This algorithm is incorporated in CCSM, a coupled atmosphere, ocean, sea ice, and land model, as one step toward integrating biogeochemical processes in this model. CCSM is designed to easily incorporate more complex BVOC models in the present framework when such models become available. Two simulations are performed: a land‐only simulation driven with prescribed atmospheric data and satellite‐derived vegetation data and a fully coupled CCSM simulation with prognostic vegetation using CCSM's dynamic vegetation model. In both cases, warm and forested regions emit more BVOC than other regions, in agreement with observations. With prescribed vegetation, global terrestrial isoprene emissions of 507 Tg C per year compare well with other model simulations. With dynamic vegetation, BVOC emissions respond to varying climate and vegetation from year to year. The interannual variability of the simulated biogenic emissions can exceed 10% of the estimated annual anthropogenic emissions provided in the IPCC emission scenarios. We include BVOC emissions within the CCSM to ultimately reduce the simulated climate uncertainty due to natural processes in this model.

An ecophysiological model of photosynthesis and respiration by forest ecosystems was used to examine CO 2 fluxes in 23 mature boreal forests near Fairbanks, Alaska. Simulated soil respiration, photosynthesis, decomposition, and moss and tree productivity were consistent with observed data. Monthly ecosystem CO 2 flux and net photosynthesis, averaged over the 23 sites, were correlated with atmospheric CO 2 concentrations and δ 13 ratios, respectively, at Barrow, Alaska, suggesting the boreal forests of Alaska play an active role in the seasonal dynamics of atmospheric CO 2 at Barrow. Only one of the 23 stands was a source of CO 2 , and the 23 stands absorbed (mean ± SE) 1173±211 g CO 2 m −2 yr −1 . Observed productivity in these forests spans the range of productivity in the circumpolar boreal forest, suggesting the simulated CO 2 fluxes are representative of the circumpolar boreal forest. If so, metabolic activity in the circumpolar boreal forest results in a significant annual uptake of CO 2 .

The role of neighborhood competition in the formation of size hierarchies was investigated using an individual—plant, spatially explicit growth model of annual plant population dynamics. Neighborhood effects were varied by having plants grow individually and in random and hexagonal spatial patterns. Resources shared between two individuals were allocated symmetrically or asymmetrically. The effect of genetic variability was controlled by first assigning constant initial masses and growth rate coefficients for all individuals and then drawing these parameters from normal frequency distributions. Size hierarchies formed when relative growth rates were positively correlated with plant mass. Neighborhood effects were an important means of inducing this relationship. When plants competed for resources, individuals had the same relative values of available growing space, relative growth rate, and mass so that size differences were enhanced over time. However, genetic variation in growth rates also caused a positive correlation between plant mass and relative growth rate. In this case, regardless of the type of resource allocation, neighborhood competition increased the variability in growth rates beyond that expected from genetic variation. These theoretical results suggest the importance of neighborhood effects in the formation of size hierarchies and imply that size structure is in part an expression of the spatial distribution and availability of resources within a stand.

Abstract In a companion paper, the authors presented a formulation and evaluation of an urban parameterization designed to represent the urban energy balance in the Community Land Model. Here the robustness of the model is tested through sensitivity studies and the model’s ability to simulate urban heat islands in different environments is evaluated. Findings show that heat storage and sensible heat flux are most sensitive to uncertainties in the input parameters within the atmospheric and surface conditions considered here. The sensitivity studies suggest that attention should be paid not only to characterizing accurately the structure of the urban area (e.g., height-to-width ratio) but also to ensuring that the input data reflect the thermal admittance properties of each of the city surfaces. Simulations of the urban heat island show that the urban model is able to capture typical observed characteristics of urban climates qualitatively. In particular, the model produces a significant heat island that increases with height-to-width ratio. In urban areas, daily minimum temperatures increase more than daily maximum temperatures, resulting in a reduced diurnal temperature range relative to equivalent rural environments. The magnitude and timing of the heat island vary tremendously depending on the prevailing meteorological conditions and the characteristics of surrounding rural environments. The model also correctly increases the Bowen ratio and canopy air temperatures of urban systems as impervious fraction increases. In general, these findings are in agreement with those observed for real urban ecosystems. Thus, the model appears to be a useful tool for examining the nature of the urban climate within the framework of global climate models.

Abstract. In many forest ecosystems, nitrogen (N) deposition enhances plant uptake of carbon dioxide, thus reducing climate warming from fossil fuel emissions. Therefore, accurately modeling how forest carbon (C) sequestration responds to N deposition is critical for understanding how future changes in N availability will influence climate. Here, we use observations of forest C response to N inputs along N deposition gradients and at five temperate forest sites with fertilization experiments to test and improve a global biogeochemical model (CLM-CN 4.0). We show that the CLM-CN plant C growth response to N deposition was smaller than observed and the modeled response to N fertilization was larger than observed. A set of modifications to the CLM-CN improved the correspondence between model predictions and observational data (1) by increasing the aboveground C storage in response to historical N deposition (1850–2004) from 14 to 34 kg C per additional kg N added through deposition and (2) by decreasing the aboveground net primary productivity response to N fertilization experiments from 91 to 57 g C m−2 yr−1. Modeled growth response to N deposition was most sensitive to altering the processes that control plant N uptake and the pathways of N loss. The response to N deposition also increased with a more closed N cycle (reduced N fixation and N gas loss) and decreased when prioritizing microbial over plant uptake of soil inorganic N. The net effect of all the modifications to the CLM-CN resulted in greater retention of N deposition and a greater role of synergy between N deposition and rising atmospheric CO2 as a mechanism governing increases in temperate forest primary production over the 20th century. Overall, testing models with both the response to gradual increases in N inputs over decades (N deposition) and N pulse additions of N over multiple years (N fertilization) allows for greater understanding of the mechanisms governing C–N coupling.

Abstract. Fires in the boreal forests of North America are generally stand-replacing, killing the majority of trees and initiating succession that may last over a century. Functional variation during succession can affect local surface energy budgets and, potentially, regional climate. Burn area across Alaska and Canada has increased in the last few decades and is projected to be substantially higher by the end of the 21st century because of a warmer climate with longer growing seasons. Here we simulated changes in forest composition due to altered burn area using a stochastic model of fire occurrence, historical fire data from national inventories, and succession trajectories derived from remote sensing. When coupled to an Earth system model, younger vegetation from increased burning cooled the high-latitude atmosphere, primarily in the winter and spring, with noticeable feedbacks from the ocean and sea ice. Results from multiple scenarios suggest that a doubling of burn area would cool the surface by 0.23 &amp;amp;pm; 0.09 °C across boreal North America during winter and spring months (December through May). This could provide a negative feedback to winter warming on the order of 3–5% for a doubling, and 14–23% for a quadrupling, of burn area. Maximum cooling occurs in the areas of greatest burning, and between February and April when albedo changes are largest and solar insolation is moderate. Further work is needed to integrate all the climate drivers from boreal forest fires, including aerosols and greenhouse gasses.

Abstract. Plants exchange greenhouse gases carbon dioxide and water with the atmosphere through the processes of photosynthesis and transpiration, making them essential in climate regulation. Carbon dioxide and water exchange are typically coupled through the control of stomatal conductance, and the parameterization in many models often predict conductance based on photosynthesis values. Some environmental conditions, like exposure to high ozone (O3) concentrations, alter photosynthesis independent of stomatal conductance, so models that couple these processes cannot accurately predict both. The goals of this study were to test direct and indirect photosynthesis and stomatal conductance modifications based on O3 damage to tulip poplar (Liriodendron tulipifera) in a coupled Farquhar/Ball-Berry model. The same modifications were then tested in the Community Land Model (CLM) to determine the impacts on gross primary productivity (GPP) and transpiration at a constant O3 concentration of 100 parts per billion (ppb). Modifying the Vcmax parameter and directly modifying stomatal conductance best predicts photosynthesis and stomatal conductance responses to chronic O3 over a range of environmental conditions. On a global scale, directly modifying conductance reduces the effect of O3 on both transpiration and GPP compared to indirectly modifying conductance, particularly in the tropics. The results of this study suggest that independently modifying stomatal conductance can improve the ability of models to predict hydrologic cycling, and therefore improve future climate predictions.

Triose phosphate utilization (TPU)-limited photosynthesis occurs when carbon export from the Calvin-Benson cycle cannot keep pace with carbon inputs and processing. This condition is poorly constrained by observations but may become an increasingly important driver of global carbon cycling under future climate scenarios. However, the consequences of including or omitting TPU limitation in models have seldom been quantified. Here, we assess the impact of changing the representation of TPU limitation on leaf- and global-scale processes. At the leaf scale, TPU limits photosynthesis at cold temperatures, high CO2 concentrations, and high light levels. Consistent with leaf-scale results, global simulations using the Community Land Model version 4.5 illustrate that the standard representation of TPU limits carbon gain under present day and future conditions, most consistently at high latitudes. If the assumed TPU limitation is doubled, further restricting photosynthesis, terrestrial ecosystem carbon pools are reduced by 9 Pg by 2100 under a business-as-usual scenario. The impact of TPU limitation on global terrestrial carbon gain suggests that CO2 concentrations may increase more than expected if models omit TPU limitation, and highlights the need to better understand when TPU limitation is important, including variation among different plant types and acclimation to temperature and CO2.

Abstract Changes in the land surface can drive large responses in the atmosphere on local, regional, and global scales. Surface properties control the partitioning of energy within the surface energy budget to fluxes of shortwave and longwave radiation, sensible and latent heat, and ground heat storage. Changes in surface energy fluxes can impact the atmosphere across scales through changes in temperature, cloud cover, and large-scale atmospheric circulation. We test the sensitivity of the atmosphere to global changes in three land surface properties: albedo, evaporative resistance, and surface roughness. We show the impact of changing these surface properties differs drastically between simulations run with an offline land model, compared to coupled land–atmosphere simulations that allow for atmospheric feedbacks associated with land–atmosphere coupling. Atmospheric feedbacks play a critical role in defining the temperature response to changes in albedo and evaporative resistance, particularly in the extratropics. More than 50% of the surface temperature response to changing albedo comes from atmospheric feedbacks in over 80% of land areas. In some regions, cloud feedbacks in response to increased evaporative resistance result in nearly 1 K of additional surface warming. In contrast, the magnitude of surface temperature responses to changes in vegetation height are comparable between offline and coupled simulations. We improve our fundamental understanding of how and why changes in vegetation cover drive responses in the atmosphere, and develop understanding of the role of individual land surface properties in controlling climate across spatial scales—critical to understanding the effects of land-use change on Earth’s climate.

The microclimates of a suburban Colorado residential landscape were studied to examine the effect of design decisions on temperature, wind speed, and relative humidity. On a hot day typical of summer, vegetated landscape elements were several degrees cooler throughout the day than non-vegetated surfaces. Across the development, dry, native grass landscapes were warmer than irrigated greenbelts and irrigated residential lawns. These data demonstrate the importance of evapotranspiration as a cooling agent in the dry, semi-arid Colorado environment. Extended meteorological measurements throughout the summer suggested housing density created microclimatic differences in the development. Heat generated by built landscape elements was readily vented from a porous neighborhood but not in a denser neighborhood. This study demonstrates that in the semi-arid Colorado environment, the choice of planting material, the design of irrigated greenbelts within a community, and the density of housing all have important consequences in creating thermally-pleasing environments.

Land surface albedo from the Community Land Model is compared to white‐sky (diffuse) and black‐sky albedo (direct at local solar noon) from MODIS. Generally, comparisons are more favorable in summer than winter, for visible waveband than near‐infrared in regions without snow cover, and for black‐ than white‐sky. In regions with extensive snow cover, the model overestimates white‐ and black‐sky albedo by up to 20% absolute. The snow‐free visible and near‐infrared black‐sky albedo is simulated quite well with biases within ±5% over most of the land surface. However, a large negative model bias was found for the Sahara Desert and Arabian Peninsula, particularly in the near‐infrared. The poorer simulation of white‐ compared to black‐sky albedo in vegetated areas implies that the model may be overestimating the increase of albedo with solar zenith angle. These results identify several areas that should have priority in further evaluating and improving albedo in the model.

Most evapotranspiration over land occurs through vegetation.The fraction of net radiation balanced by evapotranspiration depends on stomatal controls.Stomates transpire water for the leaf to assimilate carbon, depending on the canopy carbon demand, and on root uptake, if it is limiting.Canopy carbon demand in turn depends on the balancing between visible photon-driven and enzyme-driven steps in the leaf carbon physiology.The enzymedriven component is here represented by a Rubisco-related nitrogen reservoir that interacts with plant-soil nitrogen cycling and other components of a climate model.Previous canopy carbon models included in GCMs have assumed either fixed leaf nitrogen, that is, prescribed photosynthetic capacities, or an optimization between leaf nitrogen and light levels so that in either case stomatal conductance varied only with light levels and temperature.A nitrogen model is coupled to a previously derived but here modified carbon model and includes, besides the enzyme reservoir, additional plant stores for leaf structure and roots.It also includes organic and mineral reservoirs in the soil; the latter are generated, exchanged, and lost by biological fixation, deposition and fertilization, mineralization, nitrification, root uptake, denitrification, and leaching.The root nutrient uptake model is a novel and simple, but rigorous, treatment of soil transport and root physiological uptake.The other soil components are largely derived from previously published parameterizations and global budget constraints.The feasibility of applying the derived biogeochemical cycling model to climate model calculations of evapotranspiration is demonstrated through its incorporation in the Biosphere-Atmosphere Transfer Scheme land model and a 17-yr Atmospheric Model Inter comparison Project II integration with the NCAR CCM3 GCM.The derived global budgets show land net primary production (NPP), fine root carbon, and various aspects of the nitrogen cycling are reasonably consistent with past studies.Time series for monthly statistics averaged over model grid points for the Amazon evergreen forest and lower Colorado basin demonstrate the coupled interannual variability of modeled precipitation, evapotranspiration, NPP, and canopy Rubisco enzymes.

Mature boreal forest ecosystems in interior Alaska are large annual carbon sinks. Annual tree production is the largest carbon flux. A model of that combined energy, heat, and moisture exchange, tree photosynthesis and respiration, decomposition, and nitrogen mineralization was used to examine the physiological controls of the carbon balance of boreal forests. Simulated annual tree production, forest floor decomposition, nitrogen mineralization, and soil respiration were not significantly different from observed data for nine black spruce (Piceamariana (Mill.) B.S.P.), five white spruce (Piceaglauca (Moench) Voss), two quaking aspen (Populustremuloides Michx.), two paper birch (Betulapapyrifera Marsh.), and three balsam poplar (Populusbalsamifera L.) forests near Fairbanks, Alaska. The model also reproduced features of observed fertilization, soil warming, and litter transplant experiments. Net carbon uptake during tree growth was the largest simulated carbon flux, and these analyses suggest that differences in the carbon balance of these forests can be explained, in part, through key physiological parameters that link photosynthesis, carbon allocation, nitrogen requirements, litter quality, and foliage longevity. The simulations suggest that the greatest source of variation in these parameters occurs between coniferous and deciduous life-forms not among species. Simulation experiments showed that the coniferous and deciduous physiological parameters maximized annual tree production for coniferous and deciduous forests, respectively, thereby providing a physiological basis for the evolution of the different life history characteristics of deciduous and coniferous species. The strong coherency among physiological parameters allows them to be estimated from easily obtained data and may provide a basis to examine carbon fluxes over large regions.

Observations indicate that the area of lakes and wetlands in northern Africa was considerably greater during the middle Holocene than at present. Simulations are designed to examine whether expanded surface waters may have had a significant impact on the strength of the summer monsoon of northern Africa. Three experiments with the National Center for Atmospheric Research community climate model (NCAR CCM3) are analyzed, a modern and two middle Holocene (6000 years before present) simulations, one with and one without prescribed expanded surface water. There is a significant increase in the strength of the summer monsoon in the middle Holocene simulation due to the enhanced seasonal insolation cycle. The addition of surface waters result in a June, July, and August mean increase in the net surface radiation (5%), an increase in the latent heat flux (30%), a decrease in the sensible heat flux (10%), and an increase in the near‐surface specific humidity (&gt;5%) compared to the middle Holocene simulation without surface water changes. The changes in these simulated climate variables are comparable in scale to changes due to orbital forcing alone. The expanded surface waters result in a cooling of the atmosphere and anticyclonic flow over the large water bodies in summer relative to the simulation without surface water changes. The combination of increased atmospheric moisture and altered circulation results in significant changes to the precipitation distribution in northern Africa including a small increase in the zonal mean July precipitation to the north of the lakes and a decrease to the south. The geographic distribution of the precipitation with surface water changes is qualitatively in better agreement with observations than the distribution with orbital forcing alone but still does not fully match the expansion implied by observations nor the expansion required to produce the simulated middle Holocene surface waters used in this study. The results of this study suggest that surface waters were an important factor in the climate of northern Africa during the middle and early Holocene and that they must be included for accurate simulation of this climate.

Abstract. Many models that simulate the long‐term response of forests to climatic change use the assumption that northern and southern range limits are caused by the deleterious effects of cold and hot air temperatures, respectively, on individual tree growth and that growth declines symmetrically with air temperatures above and below some optimal value in between these extremes. To test the validity of this assumption, we combined physiological data for black spruce, Picea mariana, growing near the treeline in subarctic Quebec with a model of the biophysical and biochemical effects of temperature on photosynthesis. The physiological conditions allow black spruce to grow over a wider range of air temperatures than is reflected in its geographic distribution. In particular, the physiological data suggest that the northern range limit of black spruce is not caused by the direct effects of cold growing‐season air temperatures on tree growth and that growth is optimal, with respect to temperature, at the southern range limit. While pollen data indicate large geographic changes in spruce abundance with past climatic changes, the current analyses suggest that the direct effect of air temperature on individual tree growth has not caused these changes. Until we better understand the effects of air temperature on ecological processes, the efficacy of climatic change analyses must be evaluated in terms of model assumptions.